Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.
This paper presents a method for high accuracy ground target localization using a Micro Aerial Vehicle (MAV) equipped with a video camera sensor. The proposed method is based on a satellite or aerial image registration technique. The target geo-location is calculated by registering the ground target image taken from an on-board video camera with a georeferenced satellite image. This method does not require accurate knowledge of the aircraft position and attitude, therefore it is especially suitable for MAV platforms which do not have the capability to carry accurate sensors due to their limited payload weight and power resources. The paper presents results of a ground target geo-location experiment based on an image registration technique. The platform used is a MAV prototype which won the 3rd US-European Micro Aerial Vehicle Competition (MAV07). In the experiment a ground object was localized with an accuracy of 2.3 meters from a flight altitude of 70 meters. Nomenclature M AV Micro aerial vehicle s Image scale factor f Camera focal length I res Reference image resolution d MAV's ground altitude λ Ground object distance to the MAV x p , y p Pixel coordinates X i , Y i , Z i Inertial reference frame X v , Y v , Z v Vertical reference frame X b , Y b , Z b Body reference frame X g , Y g , Z g Gimbal reference frame X c , Y c , Z c Camera reference frame CCD Charge coupled cevice F OV Field of view
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